DLLNet: An Attention-Based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models

A Proof of Concept: Optimized Jawbone-Reduction Model for Mandibular Fracture Surgery

Previous research on computer-assisted jawbone reduction for mandibular fracture surgery has only focused on the relationship between fractured sections disregarding proper dental occlusion with the maxilla. To overcome malocclusion caused by overlooking dental articulation, this study aims to provide a model for jawbone reduction based on dental occlusion. After dental landmarks and fracture sectional features are extracted, the maxilla and two mandible segments are aligned first using the extracted dental landmarks. A swarm-based optimization is subsequently performed by simultaneously observing the fracture section fitting and the dental occlusion condition. The proposed method was evaluated using jawbone data of 12 subjects with simulated and real mandibular fractures. Results showed that the optimized model achieved both accurate jawbone reduction and desired dental occlusion, which may not be possible by existing methods.

TranSDFNet: Transformer-Based Truncated Signed Distance Fields for the Shape Design of Removable Partial Denture Clasps

The ever-growing aging population has led to an increasing need for removable partial dentures (RPDs) since they are typically the least expensive treatment options for partial edentulism. However, the digital design of RPDs remains challenging for dental technicians due to the variety of partially edentulous scenarios and complex combinations of denture components. To accelerate the design of RPDs, we propose a U-shape network incorporated with Transformer blocks to automatically generate RPD clasps, one of the most frequently used RPD components. Unlike existing dental restoration design algorithms, we introduce the voxel-based truncated signed distance field (TSDF) as an intermediate representation, which reduces the sensitivity of the network to resolution and contributes to more smooth reconstruction. Besides, a selective insertion scheme is proposed for solving the memory issue caused by Transformer blocks and enables the algorithm to work well in scenarios with insufficient data. We further design two weighted loss functions to filter out the noisy signals generated from the zero-gradient areas in TSDF. Ablation and comparison studies demonstrate that our algorithm outperforms state-of-the-art reconstruction methods by a large margin and can serve as an intelligent auxiliary in denture design.